Object categorization using self-organization over visual appearance
Jarmo Ilonen, Joni‐Kristian Kämäräinen · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
We propose an object categorization method which utilizes a feature structure, capturing object visual appearance, and the self-organizing map (SOM). The feature structure combines a set of spatially distant local receptive field responses with a constellation model which represents spatial relationships between the responses. The receptive field responses capture local appearance information and the spatial model generates a complete description of an object. The combination allows accurate representation of objects and their deformations. By the self-organization procedure unsupervised categorization over visual appearance of objects can be constructed. In addition, the proposed feature structure provides a reconstruction property, and thus, categorization can be used to visualize modalities of visual appearance. Categorization of real objects is demonstrated with human face images.